Datadog Case Studies How Zendesk Enables Greater Developer Productivity with AWS and Datadog

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	How Zendesk Enables Greater Developer Productivity with AWS and Datadog Datadog

How Zendesk Enables Greater Developer Productivity with AWS and Datadog

Datadog

Technology Category	Analytics & Modeling - Real Time Analytics Platform as a Service (PaaS) - Connectivity Platforms
Applicable Industries	Software
Applicable Functions	Business Operation Product Research & Development
Use Cases	Factory Operations Visibility & Intelligence Predictive Maintenance
Services	Cloud Planning, Design & Implementation Services Data Science Services
Challenge	Zendesk was transitioning to a highly dynamic, container-based environment and needed a robust monitoring solution that integrated with AWS and Kubernetes. Their existing monitoring tools created silos between teams and required manual correlation of metrics, traces, and logs, which made it difficult to resolve issues. To keep up with their customers’ evolving needs, Zendesk’s developers need the freedom to build new features quickly. Historically, Zendesk had used a monolithic, on-premises architecture for its production workloads, while its nonproduction workloads ran on Amazon Web Services (AWS). This setup created a lot of friction for their developers and made it difficult to scale. Read More
About Customer	Zendesk is a San Francisco-based customer service software company that provides support and sales products designed to help companies improve their customer experiences. More than 170,000 businesses across many industries leverage Zendesk to build stronger relationships with their customers around the world. Zendesk is committed to delivering an always-on, high-performing platform to meet the demands of their diverse user base and deliver great customer service. To keep up with their customers’ evolving needs, Zendesk’s developers need the freedom to build new features quickly. Read More
Solution	Zendesk decided to transition its production workloads to a container-based environment that ran on AWS, giving developers the modularity they needed to work on new features and accelerate development. They chose Kubernetes as their container orchestration tool, which worked well in their new cloud-based production environment on AWS. As Zendesk shifted its production workloads to AWS, they also shifted to using Datadog as their sole monitoring solution. By switching to Datadog, Zendesk was able to view their environment as a whole, instead of piecing together information from multiple monitoring solutions. Datadog easily integrates with Kubernetes clusters and gives teams the visibility they need. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Real Time Analytics

Platform as a Service (PaaS) - Connectivity Platforms

Applicable Industries

Software

Applicable Functions

Business Operation

Product Research & Development

Use Cases

Factory Operations Visibility & Intelligence

Predictive Maintenance

Services

Cloud Planning, Design & Implementation Services

Data Science Services

Challenge

Zendesk was transitioning to a highly dynamic, container-based environment and needed a robust monitoring solution that integrated with AWS and Kubernetes. Their existing monitoring tools created silos between teams and required manual correlation of metrics, traces, and logs, which made it difficult to resolve issues. To keep up with their customers’ evolving needs, Zendesk’s developers need the freedom to build new features quickly. Historically, Zendesk had used a monolithic, on-premises architecture for its production workloads, while its nonproduction workloads ran on Amazon Web Services (AWS). This setup created a lot of friction for their developers and made it difficult to scale.

About Customer

Zendesk is a San Francisco-based customer service software company that provides support and sales products designed to help companies improve their customer experiences. More than 170,000 businesses across many industries leverage Zendesk to build stronger relationships with their customers around the world. Zendesk is committed to delivering an always-on, high-performing platform to meet the demands of their diverse user base and deliver great customer service. To keep up with their customers’ evolving needs, Zendesk’s developers need the freedom to build new features quickly.

Solution

Zendesk decided to transition its production workloads to a container-based environment that ran on AWS, giving developers the modularity they needed to work on new features and accelerate development. They chose Kubernetes as their container orchestration tool, which worked well in their new cloud-based production environment on AWS. As Zendesk shifted its production workloads to AWS, they also shifted to using Datadog as their sole monitoring solution. By switching to Datadog, Zendesk was able to view their environment as a whole, instead of piecing together information from multiple monitoring solutions. Datadog easily integrates with Kubernetes clusters and gives teams the visibility they need.

Impact #1	Zendesk was able to offer more features for their customers.
Impact #2	Developers gained greater modularity and can focus more of their efforts on building applications and solving ticketing issues.
Impact #3	Zendesk was able to view their environment as a whole, instead of piecing together information from multiple monitoring solutions.

Impact #1

Zendesk was able to offer more features for their customers.

Impact #2

Developers gained greater modularity and can focus more of their efforts on building applications and solving ticketing issues.

Impact #3

Zendesk was able to view their environment as a whole, instead of piecing together information from multiple monitoring solutions.

Benefit #1	Zendesk is running 25 Kubernetes clusters split between data centers and 6 to 7 AWS Regions.

Benefit #1

Zendesk is running 25 Kubernetes clusters split between data centers and 6 to 7 AWS Regions.

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Overview

How Zendesk Enables Greater Developer Productivity with AWS and Datadog

Operational Impact

Quantitative Benefit